A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring

Abstract:Multi-trait Automated Essay Scoring (AES) requires rubric-grounded reasoning across interdependent traits, rather than isolated score prediction. Existing feedback-enhanced methods often decouple feedback from scoring or assess traits independently, weakening score--feedback consistency and rubric alignment. We propose HiFTS, a unified autoregressive framework that generates hierarchical CoT feedback before predicting trait-level and holistic scores. HiFTS distills rubric-grounded hierarchical CoT feedback from a teacher LLM and trains student models to jointly generate feedback and scores. HiFTS further applies Group Relative Policy Optimization with a composite reward balancing score agreement, calibration, feedback quality, and structural validity. At inference, a lightweight global prior provides holistic guidance to reduce drift during long-form reasoning. We also introduce CFMS-34, a Chinese multi-trait AES dataset with 951 essays annotated with holistic scores and 34 rubric-based traits. Experiments on CFMS-34 and ASAP++ show that HiFTS achieves strong holistic and trait-level scoring while producing coherent, rubric-aligned feedback.

14 pages, accepted to EMNLP 2026 Findings. Code: this https URL

Computation and Language (cs.CL)

arXiv:2608.28407 [cs.CL]

(or arXiv:2608.28407v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.28407 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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